Autonomous identification of high-contact surfaces from convolutional neural networks

Abstract

The rapid spread of the SARS-CoV-2 virus has highlighted many social interaction problems that favor the spread of disease, particularly airborne spread, which can be addressed by adjusting existing systems. Of particular interest are places where large numbers of people interact, as they become a focus for the spread of these diseases. This paper proposes and evaluates an autonomous identification scheme for certain surfaces considered high risk due to their continuous handling. These high-contact surfaces can be identified by an autonomous system to apply specific cleaning tasks to them. We evaluate three convolutional models from a proprietary dataset with a total of 2000 images ranging from wall switches to water dispensers. The objective is to identify the ideal architecture for the system. The ResNet (Residual Neural Network), DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network) models were selected due to their high performance reported in the literature. The models are evaluated with specialized metrics in non-binary classification problems, and the best scheme is selected for prototype development.